A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip

Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails

Recent Trends and Perspectives on Defect-Oriented Testing

Electronics employed in modern safety-critical systems require severe qualification during the manufacturing process and in the field, to prevent fault effects from manifesting themselves as critical failures during mission operations. Traditional fault models are not sufficient anymore to guarantee the required quality levels for chips utilized in mission-critical applications. The research community and industry have been investigating new test approaches such as device-aware test, cell-aware test, path-delay test, and even test methodologies based on the analysis of manufacturing data to move the scope from OPPM to OPPB. This special session presents four contributions, from academic researchers and industry professionals, to enable better chip quality. We present results on various activities towards this objective, including device-aware test, software-based self-test, and memory test

Towards a framework for attention cueing in instructional animations: Guidelines for research and design

This paper examines the transferability of successful cueing approaches from text and static visualization research to animations. Theories of visual attention and learning as well as empirical evidence for the instructional effectiveness of attention cueing are reviewed and, based on Mayer’s theory of multimedia learning, a framework was developed for classifying three functions for cueing: (1) selection—cues guide attention to specific locations, (2) organization—cues emphasize structure, and (3) integration—cues explicate relations between and within elements. The framework was used to structure the discussion of studies on cueing in animations. It is concluded that attentional cues may facilitate the selection of information in animations and sometimes improve learning, whereas organizational and relational cueing requires more consideration on how to enhance understanding. Consequently, it is suggested to develop cues that work in animations rather than borrowing effective cues from static representations. Guidelines for future research on attention cueing in animations are presented

Recent advances in epigenetic proteolysis targeting chimeras (Epi-PROTACs)

PROteolysis TArgeting Chimeras (PROTACs) are heterobifunctional molecules that trigger the poly-ubiquitination of the protein of interest (POI) inducing its degradation via the recruitment of the ubiquitin-proteasome system, thus suppressing the POI's intracellular levels and indirectly all its functions. Recently, one of the fields where the protein knockdown induced by PROTACs has demonstrated to serve as a promising biochemical tool and to provide new opportunities for drug discovery is the epigenetics (epi-PROTACs). A full inhibition of the functions of all domains of a specific epigenetic POI (e-POI), rather than just the block of its catalytic/single domain activity, is in fact a new more effective modality to hit an e-POI and, in principle, the complex it belongs to, and potentially to treat the related diseases, first cancer. In this review, we will present the most relevant progresses made, especially in the last two years, in the application of PROTACs technology to the three main classes of e-POIs: “writers”, “erasers” and “readers”. Emphasis will be devoted to the medicinal chemistry aspects of the epi-PROTACs design, preparation, and optimization and to the comparison with small molecule epi-drugs for both epi-targets functional annotation and potential anticancer therapy purposes